02 - AlexNet_model.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rkVdJTgVqasW"
      },
      "source": [
        "<a href=\"https://githubtocolab.com/luismateoh/proyecto-ai4eng/blob/main/02%20-%20AlexNet_model.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FBwLzU87ohQm"
      },
      "source": [
        "\n",
        "\n",
        "#  Modelo 02\n",
        "\n",
        "**Autor:** [luismateoh](https://github.com/luismateoh)<br>\n",
        "**Día de creación:** 2022/09/06<br>\n",
        "**Descripción:** Modelo AlexNet<br>\n",
        "**Url Dataset:** https://data.mendeley.com/datasets/snkd93bnjr/1\n",
        "\n",
        "Se sugiere cambiar el tipo de entorno de ejecución a GPU"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OsRiDuIhohQz"
      },
      "source": [
        "\n",
        "\n",
        "## Introducción sobre la base de datos\n",
        "\n",
        "El conjunto de datos contiene un total de 17.092 imágenes de células normales individuales, que se adquirieron utilizando el analizador CellaVision DM96 en el Laboratorio Central del Hospital Clínic de Barcelona. El conjunto de datos está organizado en los siguientes ocho grupos: neutrófilos, eosinófilos, basófilos, linfocitos, monocitos, granulocitos inmaduros (promielocitos, mielocitos y metamielocitos), eritroblastos y plaquetas o trombocitos. El tamaño de las imágenes es de 360 ​​x 363 píxeles, en formato JPG, y fueron anotadas por clínicos patólogos expertos. Las imágenes fueron capturadas de individuos sin infección, enfermedad hematológica u oncológica y libres de cualquier tratamiento farmacológico en el momento de la extracción de sangre. Este conjunto de datos etiquetados de alta calidad se puede usar para entrenar y probar modelos de aprendizaje automático y aprendizaje profundo para reconocer diferentes tipos de células sanguíneas periféricas normales. Hasta donde sabemos, este es el primer conjunto disponible públicamente con un gran número de células sanguíneas periféricas normales, por lo que se espera que sea un conjunto de datos canónico para la evaluación comparativa del modelo.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b0h1ikw6ohQ1"
      },
      "source": [
        "## Configuración del entorno y dependencias.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uRPOPynmT_cE"
      },
      "source": [
        "Se instalan las dependencias que no estan disponibles en colab, se actualiza matplotlib, y se reinicia la maquina virtual."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZPqgTsGQPm-0",
        "outputId": "c5f9a636-d2af-4e9e-f885-4899121c7b60"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: imagesize in /usr/local/lib/python3.7/dist-packages (1.4.1)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: visualkeras in /usr/local/lib/python3.7/dist-packages (0.0.2)\n",
            "Requirement already satisfied: aggdraw>=1.3.11 in /usr/local/lib/python3.7/dist-packages (from visualkeras) (1.3.15)\n",
            "Requirement already satisfied: numpy>=1.18.1 in /usr/local/lib/python3.7/dist-packages (from visualkeras) (1.21.6)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from visualkeras) (7.1.2)\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (3.5.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (2.8.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (0.11.0)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (21.3)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (3.0.9)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.21.6)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (4.38.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (7.1.2)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib) (1.4.4)\n",
            "Requirement already satisfied: typing-extensions in /usr/local/lib/python3.7/dist-packages (from kiwisolver>=1.0.1->matplotlib) (4.1.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7->matplotlib) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install imagesize\n",
        "!pip install visualkeras\n",
        "!pip install matplotlib --upgrade"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XGfeyrwKT_cG"
      },
      "source": [
        "Se importan las dependencias necesarias para ejecutar el notebook."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "WT74-UZyohQ2"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import os.path\n",
        "import imagesize\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from pathlib import Path\n",
        "from collections import Counter\n",
        "from ast import literal_eval\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "import keras \n",
        "from keras import layers\n",
        "from keras.models import Sequential\n",
        "from tensorflow.keras import Input\n",
        "from keras.layers import Rescaling\n",
        "from keras.layers import Conv2D\n",
        "from keras.layers import MaxPooling2D\n",
        "from keras.layers import Flatten\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Dropout\n",
        "from keras.layers import BatchNormalization\n",
        "\n",
        "\n",
        "import visualkeras\n",
        "\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E9kNOy_eohQ5"
      },
      "source": [
        "## Carga de la base de datos\n",
        "\n",
        "### Descarga de datos sin procesar\n",
        "\n",
        "Primero, descarguemos el archivo ZIP de 265M de los datos sin procesar:\n",
        "\n",
        "Si la descarga falla o el link esta roto, revisar la pagina https://data.mendeley.com/datasets/snkd93bnjr/1 para obtener el enlace actualizado. \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_UoC-7fbohQ7",
        "outputId": "79e54550-ac0c-4464-84b9-f670947a7576"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
            "100  265M  100  265M    0     0  38.0M      0  0:00:06  0:00:06 --:--:-- 34.8M\n"
          ]
        }
      ],
      "source": [
        "!curl -O https://prod-dcd-datasets-cache-zipfiles.s3.eu-west-1.amazonaws.com/snkd93bnjr-1.zip\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FGO8DAv_ohQ8",
        "outputId": "fef1301e-fdf3-4c51-8a57-f43af28ae8be"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "__MACOSX  PBC_dataset_normal_DIB  sample_data\n"
          ]
        }
      ],
      "source": [
        "!unzip -q snkd93bnjr-1.zip\n",
        "!unzip -q PBC_dataset_normal_DIB.zip\n",
        "!rm -f snkd93bnjr-1.zip PBC_dataset_normal_DIB.zip\n",
        "!ls\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YgESie9MohQ9"
      },
      "source": [
        "Ahora tenemos una carpeta `PBC_dataset_normal_DIB` que contiene ocho subcarpetas. Cada subcarpeta contiene archivos de imagen para cada categoría\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9z0zLlUVohQ-",
        "outputId": "7a1e9480-6c34-46b8-c474-7655f64a7b13"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "basophil    erythroblast  lymphocyte  neutrophil\n",
            "eosinophil  ig\t\t  monocyte    platelet\n"
          ]
        }
      ],
      "source": [
        "!ls PBC_dataset_normal_DIB  \n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uKr5EgyWohQ_"
      },
      "source": [
        "### Filtrar imágenes corruptas\n",
        "\n",
        "Cuando se trabaja con una gran cantidad de datos de imágenes del mundo real, las imágenes corruptas son comunes.\n",
        "Filtramos las imágenes mal codificadas que no tengan la cadena \"JFIF\" en su cabecera.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YXWi7rMqohRA",
        "outputId": "47d1260e-2db4-44cd-bc3b-5811f9456b9e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Deleted 1 images\n"
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "num_skipped = 0\n",
        "for folder_name in (\"basophil\", \"erythroblast\", \"lymphocyte\", \"neutrophil\", \"eosinophil\", \"ig\", \"monocyte\", \"platelet\"):\n",
        "    folder_path = os.path.join(\"PBC_dataset_normal_DIB\", folder_name)\n",
        "    for fname in os.listdir(folder_path):\n",
        "        fpath = os.path.join(folder_path, fname)\n",
        "        try:\n",
        "            fobj = open(fpath, \"rb\")\n",
        "            is_jfif = tf.compat.as_bytes(\"JFIF\") in fobj.peek(10)\n",
        "        finally:\n",
        "            fobj.close()\n",
        "\n",
        "        if not is_jfif:\n",
        "            num_skipped += 1\n",
        "            # Delete corrupted image\n",
        "            os.remove(fpath)\n",
        "\n",
        "print(\"Deleted %d images\" % num_skipped)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A-QK5FTYohRB"
      },
      "source": [
        "## Generación del `Dataset`\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PR1j8gavZMKC"
      },
      "source": [
        "Creamos primero un dataset con el 80% de las imagenes para el entrenamiento, y el 20 restante quedarán de validación, que a su vez partiremos para obtener un tercer conjuto de datos de test."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2v7G6UCohRC",
        "outputId": "3ba72f12-3ba3-4480-9fc9-409dcd82ba37"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Firt Dataset partition\n",
            "Found 17092 files belonging to 8 classes.\n",
            "Using 13674 files for training.\n",
            "Found 17092 files belonging to 8 classes.\n",
            "Using 3418 files for validation.\n",
            "\n",
            "Second Dataset partition\n",
            "Train dataset: 13674 images.\n",
            "Validation dataset: 3258 images.\n",
            "Test dataset: 160 images.\n"
          ]
        }
      ],
      "source": [
        "\n",
        "#Para el modelo que usaremos debe ser de (227,227)\n",
        "image_size = (224, 224)\n",
        "batch_size = 32\n",
        "\n",
        "print(\"Firt Dataset partition\")\n",
        "train_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    \"PBC_dataset_normal_DIB\",\n",
        "    validation_split=0.20,\n",
        "    subset=\"training\",\n",
        "    seed=2022,\n",
        "    image_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    label_mode='categorical'\n",
        ")\n",
        "val_ds = tf.keras.preprocessing.image_dataset_from_directory(\n",
        "    \"PBC_dataset_normal_DIB\",\n",
        "    labels='inferred',\n",
        "    validation_split=0.20,\n",
        "    subset=\"validation\",\n",
        "    seed=2022,\n",
        "    image_size=image_size,\n",
        "    batch_size=batch_size,\n",
        "    label_mode='categorical'\n",
        ")\n",
        "\n",
        "\n",
        "print(\"\\nSecond Dataset partition\")\n",
        "\n",
        "test_dataset = val_ds.take(5)\n",
        "val_ds = val_ds.skip(5)\n",
        "\n",
        "print(f\"Train dataset: {len(np.concatenate([i for x, i in train_ds], axis=0))} images.\")\n",
        "print(f\"Validation dataset: {len(np.concatenate([i for x, i in val_ds], axis=0))} images.\")\n",
        "print(f\"Test dataset: {len(np.concatenate([i for x, i in test_dataset], axis=0))} images.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K0gWqmpdohRE"
      },
      "source": [
        "## Usando el aumento de datos de imagen\n",
        "\n",
        "Cuando no tiene un conjunto de datos de imagen grande, es una buena práctica\n",
        "introduce la diversidad de la muestra mediante la aplicación de transformaciones aleatorias pero realistas a la\n",
        "imágenes de entrenamiento, como volteo horizontal aleatorio o pequeñas rotaciones aleatorias. Este\n",
        "ayuda a exponer el modelo a diferentes aspectos de los datos de entrenamiento mientras se disminuye sobreajuste.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "Ke_wLv5ZohRF"
      },
      "outputs": [],
      "source": [
        "data_augmentation = keras.Sequential(\n",
        "    [\n",
        "        layers.RandomFlip(\"horizontal_and_vertical\"),\n",
        "        layers.RandomRotation(0.2),\n",
        "        layers.RandomZoom(.3, .3)\n",
        "\n",
        "    ]\n",
        ")\n",
        "\n",
        "augmented_train_ds = train_ds.map(\n",
        "  lambda x, y: (data_augmentation(x, training=True), y))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vZhXZ4I9cPoi"
      },
      "source": [
        "## Configurar el conjunto de datos para el rendimiento\n",
        "\n",
        "Asegurémonos de usar la captación previa almacenada en búfer para que podamos obtener datos del disco sin tener bloqueos de I/O:\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "62n-RAUgcPaR"
      },
      "outputs": [],
      "source": [
        "train_ds = augmented_train_ds.prefetch(buffer_size=32)\n",
        "val_ds = val_ds.prefetch(buffer_size=32)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EOurend8ohRJ"
      },
      "source": [
        "## Construir un modelo\n",
        "\n",
        "Construiremos una versión de AlexNet\n",
        "\n",
        "Tenga en cuenta que:\n",
        "\n",
        "- Comenzamos el modelo con una capa de `Reescalado`.*\n",
        "- Incluimos una capa `Dropout` antes de la capa de clasificación final.*\n",
        "   \n",
        "  **No hacen parte del modelo original*\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OmxuniSQohRH"
      },
      "source": [
        "Nuestras imágenes ya están en un tamaño estándar (h,w), ya que se están produciendo como\n",
        "lotes `float32` contiguos por nuestro conjunto de datos. Sin embargo, sus valores de canal RGB están en\n",
        " el rango `[0, 255]`. Esto no es ideal para una red neuronal;\n",
        "en general, debe buscar que sus valores de entrada sean pequeños. Aquí, lo haremos\n",
        "estandarizando los valores para que estén en `[0, 1]` mediante el uso de una capa de `Reescalado` al comienzo de\n",
        " nuestro modelo\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MfcJgSgtzn4Y"
      },
      "source": [
        "Dropout es un metodo que desactiva un numero de neuronas de una red neuronal de forma aleatoria. En cada iteración de la red neuronal dropout desactivara diferentes neuronas, las neuronas desactivadas no se toman en cuenta para el `forwardpropagation` ni para el `backwardpropagation` lo que obliga a las neuronas cercanas a no depender tanto de las neuronas desactivadas. Este metodo ayuda a reducir el `overfitting`. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QWK02tppohRK",
        "outputId": "57cad7f7-fa6f-4684-8253-a8bd2c0d2889"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"AlexNet\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " rescaling (Rescaling)       (None, 224, 224, 3)       0         \n",
            "                                                                 \n",
            " conv2d (Conv2D)             (None, 54, 54, 96)        34944     \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 27, 27, 96)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " batch_normalization (BatchN  (None, 27, 27, 96)       384       \n",
            " ormalization)                                                   \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 17, 17, 256)       2973952   \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 8, 8, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_1 (Batc  (None, 8, 8, 256)        1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 6, 6, 384)         885120    \n",
            "                                                                 \n",
            " batch_normalization_2 (Batc  (None, 6, 6, 384)        1536      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 4, 4, 384)         1327488   \n",
            "                                                                 \n",
            " batch_normalization_3 (Batc  (None, 4, 4, 384)        1536      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 2, 2, 256)         884992    \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 1, 1, 256)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_4 (Batc  (None, 1, 1, 256)        1024      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 256)               0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 4096)              1052672   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 4096)              0         \n",
            "                                                                 \n",
            " batch_normalization_5 (Batc  (None, 4096)             16384     \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 4096)              16781312  \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 4096)              0         \n",
            "                                                                 \n",
            " batch_normalization_6 (Batc  (None, 4096)             16384     \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 1000)              4097000   \n",
            "                                                                 \n",
            " dropout_2 (Dropout)         (None, 1000)              0         \n",
            "                                                                 \n",
            " batch_normalization_7 (Batc  (None, 1000)             4000      \n",
            " hNormalization)                                                 \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 8)                 8008      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 28,087,760\n",
            "Trainable params: 28,066,624\n",
            "Non-trainable params: 21,136\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model = Sequential()\n",
        "model._name=\"AlexNet\"\n",
        "#Input \n",
        "model.add(Input(shape=image_size + (3,)))\n",
        "#Layer Rescaling\n",
        "model.add(Rescaling(1.0 / 255))\n",
        "# Convolution Step 1\n",
        "model.add(Conv2D(96, 11, strides = (4, 4), padding = 'valid', activation = 'relu'))\n",
        "\n",
        "# Max Pooling Step 1\n",
        "model.add(MaxPooling2D(pool_size = (2, 2), strides = (2, 2), padding = 'valid'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "# Convolution Step 2\n",
        "model.add(Conv2D(256, 11, strides = (1, 1), padding='valid', activation = 'relu'))\n",
        "\n",
        "# Max Pooling Step 2\n",
        "model.add(MaxPooling2D(pool_size = (2, 2), strides = (2, 2), padding='valid'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "# Convolution Step 3\n",
        "model.add(Conv2D(384, 3, strides = (1, 1), padding='valid', activation = 'relu'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "# Convolution Step 4\n",
        "model.add(Conv2D(384, 3, strides = (1, 1), padding='valid', activation = 'relu'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "# Convolution Step 5\n",
        "model.add(Conv2D(256, 3, strides=(1,1), padding='valid', activation = 'relu'))\n",
        "\n",
        "# Max Pooling Step 3\n",
        "model.add(MaxPooling2D(pool_size = (2, 2), strides = (2, 2), padding = 'valid'))\n",
        "model.add(BatchNormalization())\n",
        "\n",
        "# Flattening Step\n",
        "model.add(Flatten())\n",
        "\n",
        "# Full Connection Step\n",
        "model.add(Dense(units = 4096, activation = 'relu'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(units = 4096, activation = 'relu'))\n",
        "model.add(Dropout(0.4))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(units = 1000, activation = 'relu'))\n",
        "model.add(Dropout(0.2))\n",
        "model.add(BatchNormalization())\n",
        "model.add(Dense(units = 8, activation = 'softmax'))\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "udyKtAYGfWlf",
        "outputId": "be17484e-9c54-4e87-c92f-7d8554f4765d"
      },
      "outputs": [
        {
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            "text/plain": [
              "<PIL.Image.Image image mode=RGBA size=3546x1231 at 0x7F5AF06CE1D0>"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "visualkeras.layered_view(model,legend=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y5vGWUpDohRK"
      },
      "source": [
        "## Entrenar el modelo\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rDUTPTQXohRL",
        "outputId": "9f6d3494-d26f-4af8-dce7-c7ff90d533b1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "428/428 [==============================] - 182s 399ms/step - loss: 1.0846 - accuracy: 0.6330 - val_loss: 4.2854 - val_accuracy: 0.2287\n",
            "Epoch 2/50\n",
            "428/428 [==============================] - 168s 393ms/step - loss: 0.5545 - accuracy: 0.8032 - val_loss: 28.8575 - val_accuracy: 0.0862\n",
            "Epoch 3/50\n",
            "428/428 [==============================] - 169s 393ms/step - loss: 0.4557 - accuracy: 0.8393 - val_loss: 5.8982 - val_accuracy: 0.1470\n",
            "Epoch 4/50\n",
            "428/428 [==============================] - 167s 389ms/step - loss: 0.4046 - accuracy: 0.8591 - val_loss: 2.5357 - val_accuracy: 0.4564\n",
            "Epoch 5/50\n",
            "428/428 [==============================] - 169s 394ms/step - loss: 0.3687 - accuracy: 0.8725 - val_loss: 1.6670 - val_accuracy: 0.5522\n",
            "Epoch 6/50\n",
            "428/428 [==============================] - 166s 388ms/step - loss: 0.3546 - accuracy: 0.8820 - val_loss: 0.6881 - val_accuracy: 0.7603\n",
            "Epoch 7/50\n",
            "428/428 [==============================] - 166s 387ms/step - loss: 0.3258 - accuracy: 0.8898 - val_loss: 3.5695 - val_accuracy: 0.4334\n",
            "Epoch 8/50\n",
            "428/428 [==============================] - 164s 383ms/step - loss: 0.3026 - accuracy: 0.8968 - val_loss: 0.8395 - val_accuracy: 0.7673\n",
            "Epoch 9/50\n",
            "428/428 [==============================] - 163s 381ms/step - loss: 0.2901 - accuracy: 0.9008 - val_loss: 0.8959 - val_accuracy: 0.7320\n",
            "Epoch 10/50\n",
            "428/428 [==============================] - 161s 376ms/step - loss: 0.2737 - accuracy: 0.9071 - val_loss: 1.2790 - val_accuracy: 0.6080\n",
            "Epoch 11/50\n",
            "428/428 [==============================] - 161s 374ms/step - loss: 0.2556 - accuracy: 0.9141 - val_loss: 2.6761 - val_accuracy: 0.5270\n",
            "Epoch 12/50\n",
            "428/428 [==============================] - 161s 375ms/step - loss: 0.2351 - accuracy: 0.9207 - val_loss: 11.4730 - val_accuracy: 0.2066\n",
            "Epoch 13/50\n",
            "428/428 [==============================] - 161s 376ms/step - loss: 0.2412 - accuracy: 0.9177 - val_loss: 0.8435 - val_accuracy: 0.7750\n",
            "Epoch 14/50\n",
            "428/428 [==============================] - 161s 376ms/step - loss: 0.2807 - accuracy: 0.9019 - val_loss: 0.2029 - val_accuracy: 0.9297\n",
            "Epoch 15/50\n",
            "428/428 [==============================] - 164s 382ms/step - loss: 0.2447 - accuracy: 0.9166 - val_loss: 2.8009 - val_accuracy: 0.4537\n",
            "Epoch 16/50\n",
            "428/428 [==============================] - 162s 377ms/step - loss: 0.2214 - accuracy: 0.9224 - val_loss: 0.7631 - val_accuracy: 0.7591\n",
            "Epoch 17/50\n",
            "428/428 [==============================] - 161s 376ms/step - loss: 0.2166 - accuracy: 0.9282 - val_loss: 1.4076 - val_accuracy: 0.6077\n",
            "Epoch 18/50\n",
            "428/428 [==============================] - 161s 376ms/step - loss: 0.2094 - accuracy: 0.9287 - val_loss: 0.4256 - val_accuracy: 0.8677\n",
            "Epoch 19/50\n",
            "428/428 [==============================] - 161s 376ms/step - loss: 0.1999 - accuracy: 0.9328 - val_loss: 0.4874 - val_accuracy: 0.8358\n",
            "Epoch 20/50\n",
            "428/428 [==============================] - 162s 378ms/step - loss: 0.2023 - accuracy: 0.9329 - val_loss: 3.1692 - val_accuracy: 0.4487\n",
            "Epoch 21/50\n",
            "428/428 [==============================] - 161s 376ms/step - loss: 0.1812 - accuracy: 0.9389 - val_loss: 0.3399 - val_accuracy: 0.8828\n",
            "Epoch 22/50\n",
            "428/428 [==============================] - 161s 376ms/step - loss: 0.1788 - accuracy: 0.9407 - val_loss: 1.2699 - val_accuracy: 0.6590\n",
            "Epoch 23/50\n",
            "428/428 [==============================] - 161s 375ms/step - loss: 0.1891 - accuracy: 0.9362 - val_loss: 3.6132 - val_accuracy: 0.3048\n",
            "Epoch 24/50\n",
            "428/428 [==============================] - 161s 376ms/step - loss: 0.1684 - accuracy: 0.9424 - val_loss: 0.3396 - val_accuracy: 0.8901\n",
            "Epoch 25/50\n",
            "428/428 [==============================] - 161s 375ms/step - loss: 0.1755 - accuracy: 0.9416 - val_loss: 2.1471 - val_accuracy: 0.5749\n",
            "Epoch 26/50\n",
            "428/428 [==============================] - 161s 375ms/step - loss: 0.1515 - accuracy: 0.9485 - val_loss: 0.3823 - val_accuracy: 0.9061\n",
            "Epoch 27/50\n",
            "428/428 [==============================] - 161s 375ms/step - loss: 0.1651 - accuracy: 0.9438 - val_loss: 0.2265 - val_accuracy: 0.9251\n",
            "Epoch 28/50\n",
            "428/428 [==============================] - 161s 374ms/step - loss: 0.1761 - accuracy: 0.9419 - val_loss: 0.8996 - val_accuracy: 0.7769\n",
            "Epoch 29/50\n",
            "428/428 [==============================] - 162s 378ms/step - loss: 0.1382 - accuracy: 0.9518 - val_loss: 0.2078 - val_accuracy: 0.9355\n",
            "Epoch 30/50\n",
            "428/428 [==============================] - 163s 379ms/step - loss: 0.1561 - accuracy: 0.9495 - val_loss: 0.8871 - val_accuracy: 0.7231\n",
            "Epoch 31/50\n",
            "428/428 [==============================] - 163s 379ms/step - loss: 0.1526 - accuracy: 0.9501 - val_loss: 0.3190 - val_accuracy: 0.8834\n",
            "Epoch 32/50\n",
            "428/428 [==============================] - 161s 375ms/step - loss: 0.1634 - accuracy: 0.9468 - val_loss: 0.7009 - val_accuracy: 0.8042\n",
            "Epoch 33/50\n",
            "428/428 [==============================] - 161s 375ms/step - loss: 0.1374 - accuracy: 0.9544 - val_loss: 2.0057 - val_accuracy: 0.5092\n",
            "Epoch 34/50\n",
            "428/428 [==============================] - 161s 375ms/step - loss: 0.1316 - accuracy: 0.9561 - val_loss: 0.7196 - val_accuracy: 0.8257\n",
            "Epoch 35/50\n",
            "428/428 [==============================] - 161s 374ms/step - loss: 0.1204 - accuracy: 0.9566 - val_loss: 2.0751 - val_accuracy: 0.5961\n",
            "Epoch 36/50\n",
            "428/428 [==============================] - 161s 375ms/step - loss: 0.1341 - accuracy: 0.9549 - val_loss: 1.5735 - val_accuracy: 0.6455\n",
            "Epoch 37/50\n",
            "428/428 [==============================] - 163s 379ms/step - loss: 0.1130 - accuracy: 0.9649 - val_loss: 0.2831 - val_accuracy: 0.9039\n",
            "Epoch 38/50\n",
            "428/428 [==============================] - 162s 378ms/step - loss: 0.1234 - accuracy: 0.9594 - val_loss: 4.8374 - val_accuracy: 0.4929\n",
            "Epoch 39/50\n",
            "428/428 [==============================] - 161s 375ms/step - loss: 0.1176 - accuracy: 0.9620 - val_loss: 0.1208 - val_accuracy: 0.9573\n",
            "Epoch 40/50\n",
            "428/428 [==============================] - 161s 376ms/step - loss: 0.1135 - accuracy: 0.9618 - val_loss: 1.1587 - val_accuracy: 0.6654\n",
            "Epoch 41/50\n",
            "428/428 [==============================] - 161s 375ms/step - loss: 0.1319 - accuracy: 0.9569 - val_loss: 0.1679 - val_accuracy: 0.9423\n",
            "Epoch 42/50\n",
            "428/428 [==============================] - 161s 376ms/step - loss: 0.1023 - accuracy: 0.9657 - val_loss: 3.2987 - val_accuracy: 0.4518\n",
            "Epoch 43/50\n",
            "428/428 [==============================] - 160s 374ms/step - loss: 0.1039 - accuracy: 0.9636 - val_loss: 0.4109 - val_accuracy: 0.8656\n",
            "Epoch 44/50\n",
            "428/428 [==============================] - 161s 374ms/step - loss: 0.1051 - accuracy: 0.9644 - val_loss: 2.2814 - val_accuracy: 0.5157\n",
            "Epoch 45/50\n",
            "428/428 [==============================] - 161s 374ms/step - loss: 0.1016 - accuracy: 0.9649 - val_loss: 2.1685 - val_accuracy: 0.5608\n",
            "Epoch 46/50\n",
            "428/428 [==============================] - 160s 374ms/step - loss: 0.1098 - accuracy: 0.9658 - val_loss: 0.4301 - val_accuracy: 0.8870\n",
            "Epoch 47/50\n",
            "428/428 [==============================] - 160s 374ms/step - loss: 0.0975 - accuracy: 0.9665 - val_loss: 0.1535 - val_accuracy: 0.9417\n",
            "Epoch 48/50\n",
            "428/428 [==============================] - 160s 373ms/step - loss: 0.1113 - accuracy: 0.9635 - val_loss: 3.7142 - val_accuracy: 0.4411\n",
            "Epoch 49/50\n",
            "428/428 [==============================] - 165s 385ms/step - loss: 0.1201 - accuracy: 0.9586 - val_loss: 0.9759 - val_accuracy: 0.7502\n",
            "Epoch 50/50\n",
            "428/428 [==============================] - 164s 383ms/step - loss: 0.1008 - accuracy: 0.9639 - val_loss: 0.6973 - val_accuracy: 0.8085\n"
          ]
        }
      ],
      "source": [
        "epochs = 50\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\"save_at_{epoch}.h5\"),\n",
        "]\n",
        "\n",
        "model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics = ['accuracy'])\n",
        "hist = model.fit(train_ds, \n",
        "                 epochs=epochs, \n",
        "                 callbacks=callbacks, \n",
        "                 validation_data=val_ds)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nOH3YYfDohRL"
      },
      "source": [
        "## Evaluación y metricas ejecutando inferencia sobre nuevos datos"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pmI3ZDJ7iOkm"
      },
      "source": [
        "### Resumen de precisión y función de perdida del modelo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 367
        },
        "id": "yXTLJXY8iHRo",
        "outputId": "237a7060-5345-42c8-f5b3-9f463ee5da4e"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5af0241a90>"
            ]
          },
          "execution_count": 22,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1440x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig, (ax1, ax2) = plt.subplots(1, 2, sharex=False, sharey=False)\n",
        "fig.set_size_inches(20,5)\n",
        "\n",
        "ax1.plot(hist.history['accuracy'], label='train')\n",
        "ax1.plot(hist.history['val_accuracy'],label='test')\n",
        "ax1.set_title('model accuracy')\n",
        "ax1.set_ylabel('accuracy')\n",
        "ax1.set_xlabel('epoch')\n",
        "ax1.legend(loc='upper left')\n",
        "\n",
        "ax2.plot(hist.history['loss'], label='train')\n",
        "ax2.plot(hist.history['val_loss'],label='test')\n",
        "ax2.set_title('model loss')\n",
        "ax2.set_ylabel('loss')\n",
        "ax2.set_xlabel('epoch')\n",
        "ax2.legend(loc='upper right')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i-jJGUQMyFfL"
      },
      "source": [
        "### Clasificación de datos nuevos\n",
        "Tenga en cuenta que el aumento y la eliminación de datos están inactivos en el momento de la inferencia."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1R4kGJugMDd",
        "outputId": "4e30b9be-b504-4257-e76a-36638d61b0d7"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 290ms/step\n",
            "1/1 [==============================] - 0s 28ms/step\n",
            "1/1 [==============================] - 0s 30ms/step\n",
            "1/1 [==============================] - 0s 43ms/step\n",
            "1/1 [==============================] - 0s 35ms/step\n"
          ]
        }
      ],
      "source": [
        "labels_list = sorted(['erythroblast','eosinophil','monocyte','platelet','lymphocyte',\n",
        " 'neutrophil','ig','basophil'])\n",
        "\n",
        "Y_pred = np.array([])\n",
        "Y_labels =  np.array([])\n",
        "for x, y in test_dataset:\n",
        "  Y_pred = np.concatenate([Y_pred, np.argmax(model.predict(x), axis = -1)])\n",
        "  Y_labels = np.concatenate([Y_labels, np.argmax(y.numpy(), axis=-1)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xE398-Ytgrno"
      },
      "source": [
        "### Matriz de confusión"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 755
        },
        "id": "QVCy7tF9ohRL",
        "outputId": "ced23f1d-9da1-4978-fb14-ba36a89d7bd0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "cf_matrix = confusion_matrix(Y_labels, Y_pred,  normalize=\"true\")\n",
        "plt.figure(figsize = (12,10))\n",
        "heatmap = sns.heatmap(cf_matrix, \n",
        "            annot=True, \n",
        "            xticklabels = labels_list, \n",
        "            yticklabels = labels_list,\n",
        "            cmap=plt.cm.Greens\n",
        "            )\n",
        "plt.tight_layout()\n",
        "heatmap.set_ylabel('True label', fontsize=10)\n",
        "heatmap.set_xlabel('Predicted Label', fontsize=10)\n",
        "\n",
        "plt.title('Confusion Matrix')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "loDIZhCbimY5"
      },
      "source": [
        "### Reporte de la clasificación"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_DgzWOlikTU",
        "outputId": "9030b81d-a211-4d2d-a8c8-cd90ef680ea2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Classification Report\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "    basophil       0.89      1.00      0.94         8\n",
            "  eosinophil       0.74      1.00      0.85        25\n",
            "erythroblast       1.00      0.67      0.80         9\n",
            "          ig       0.58      0.96      0.72        27\n",
            "  lymphocyte       1.00      0.77      0.87        13\n",
            "    monocyte       1.00      0.17      0.29        12\n",
            "  neutrophil       0.97      0.81      0.89        43\n",
            "    platelet       1.00      0.78      0.88        23\n",
            "\n",
            "    accuracy                           0.81       160\n",
            "   macro avg       0.90      0.77      0.78       160\n",
            "weighted avg       0.87      0.81      0.80       160\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print('Classification Report\\n')\n",
        "print(classification_report(Y_labels, Y_pred, target_names=labels_list))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "provenance": []
    },
    "gpuClass": "standard",
    "kernelspec": {
      "display_name": "Python 3.11.0 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.0"
    },
    "vscode": {
      "interpreter": {
        "hash": "c261aea317cc0286b3b3261fbba9abdec21eaa57589985bb7a274bf54d6cc0a7"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}